AI Diploma Program

A hands-on, notebook-based curriculum for the Artificial Intelligence Technology diploma (AIAT track): 12 courses, 944 training hours, delivered over 2 official semesters.

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Courses

Semester 1 (AIAT 111–116)

Semester 2 (AIAT 121–126)

Total: 944 training hours across 2 semesters. Courses are taken in order, 01 through 12.

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Quick Start

1. Set up the environment

cd "AI Diploma"
python3 -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt

2. Register the Jupyter kernel

python -m ipykernel install --user --name ai-diploma --display-name "AI Diploma"

Most notebooks in this repository use the ai-diploma kernel.

TensorFlow note: TensorFlow does not ship a wheel for this venv's Python. The TensorFlow/Keras notebooks in Course 01 and Course 08 run on a separate kernel named tfenv (a Python 3.13 environment with TensorFlow installed). See docs/SETUP_GUIDE.md for how to create it.

3. Launch Jupyter and open the first course

jupyter lab

Open Course 01/START_HERE.md and follow it.

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Repository Structure

AI Diploma/
├── Course 01/ … Course 12/       # One folder per course (AIAT 111–126)
│   ├── START_HERE.md             # Read this first in every course
│   ├── README.md                 # Course overview and unit map
│   ├── unit1-…/ … unit5-…/       # Units, each with:
│   │   ├── examples/             #   numbered notebooks (01_…, 02_…, …)
│   │   └── exercises/            #   practice exercises
│   ├── QUIZZES/                  # Unit quizzes
│   ├── ASSESSMENTS/              # Final exam / course assessment
│   ├── PROJECTS/                 # Course projects
│   ├── CASE_STUDIES/             # Applied case studies
│   ├── PRESENTATIONS/            # Unit slide decks
│   ├── DOCS/                     # Extra docs (Colab setup, FAQ) — some courses
│   └── STUDENT_PROGRESS_CHECKLIST.md
├── docs/                         # Program-level guides (setup, navigation, troubleshooting)
├── tools/                        # Maintenance and verification scripts
├── requirements.txt              # Student environment baseline
└── README.md                     # This file

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Learning Path

Every course follows the same numbered path:

START_HERE.md → numbered examples (01 → NN) → exercise → quiz → assessment

1. START_HERE.md — course setup and the exact order to follow.
2. Examples — study and run the numbered notebooks in each unit, in order.
3. Exercises — complete the practice notebooks in each unit's exercises/ folder.
4. Quizzes — take the unit quiz before moving to the next unit.
5. Assessment — finish with the course assessment in ASSESSMENTS/.

Solutions and answer keys are released by your instructor — they are not part of this repository.

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Tech Stack

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Prerequisites

• A computer running Windows, macOS, or Linux (8 GB RAM minimum)
• Python 3 (see docs/SETUP_GUIDE.md for versions)
• No prior AI experience required — Course 01 starts from the beginning

GPU is optional: the courses that benefit from one (05, 08, 10) include Google Colab instructions. See docs/GPU_REQUIREMENTS_SUMMARY.md.

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Documentation

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Last updated: 2026-08